"""
Copyright 2020 Huawei Technologies Co., Ltd
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import torch
import models.resnet_0_6_0 as resnet_0_6_0
import torch.onnx
from collections import OrderedDict
import os


def proc_node_module(checkpoint, AttrName):
    new_state_dict = OrderedDict()
    for k, v in checkpoint[AttrName].items():
        if(k[0:7] == "module."):
            name = k[7:]
        else:
            name = k[0:]
        new_state_dict[name] = v
    return new_state_dict


def convert(model_path, onnx_save):
    checkpoint = torch.load(model_path, map_location='cpu')
    checkpoint['state_dict'] = proc_node_module(checkpoint, 'state_dict')
    model = resnet_0_6_0.wide_resnet101_2(num_classes=1000)
    model.load_state_dict(checkpoint['state_dict'])
    model.eval()
    input_names = ["actual_input_1"]
    output_names = ["output1"]
    dummy_input = torch.randn(1, 3, 224, 224)
    if len(onnx_save) > 0:
        save_path = os.path.join(onnx_save, "wide_resnet101_2_npu_16.onnx")
    else:
        save_path = "wide_resnet101_2_npu_16.onnx"
    print(save_path)
    torch.onnx.export(model, dummy_input, save_path
                     , input_names=input_names, output_names=output_names
                     , opset_version=11)

if __name__ == '__main__':
    convert('./model_best.pth.tar', '')

